Skin Gene Expression Is Prognostic for the Trajectory of Skin Disease in Patients With Diffuse Cutaneous Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: At present, there are no clinical or laboratory measures that accurately forecast the progression of skin fibrosis and organ involvement in patients with systemic sclerosis (SSc). The goal of this study was to identify skin biomarkers that could be prognostic for the progression of skin fibrosis in patients with early diffuse cutaneous SSc (dcSSc). METHODS: We analyzed clinical data and gene expression in skin biopsy samples from 38 placebo-treated patients, part of the Roche Safety and Efficacy of Subcutaneous Tocilizumab in Adults with Systemic Sclerosis (FASSCINATE) phase II study of tocilizumab in SSc. RNA samples were analyzed using nCounter. A trajectory model based on a modified Rodnan skin thickness score was used to describe 3 skin disease trajectories over time. We examined the association of skin gene expression with skin score trajectory groups, by chi-square test. Logistic regression was used to examine the prognostic power of each gene identified. RESULTS: We found that placebo-treated patients with high expression of messenger RNA for CD14, SERPINE1, IL13RA1, CTGF, and OSMR at baseline were more likely to have progressive skin score trajectories. We also found that those genes were prognostic for the risk of skin progression and that IL13RA1, OSMR, and SERPINE1 performed the best. CONCLUSION: Skin gene expression of biomarkers associated with macrophages (CD14, IL13RA1) and transforming growth factor β activation (SERPINE1, CTGF, OSMR) are prognostic for progressive skin disease in patients with dcSSc. These biomarkers may provide guidance in decision-making about which patients should be considered for aggressive therapies and/or for clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".